{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_table('./log.txt', header = None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.columns = ['id', 'api', 'count', 'res_time_sum', 'res_time_min', 'res_time_max', 'res_time_avg', 'interval', 'created_time']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>api</th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>interval</th>\n",
       "      <th>created_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019162542</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>8</td>\n",
       "      <td>1057.31</td>\n",
       "      <td>88.75</td>\n",
       "      <td>177.72</td>\n",
       "      <td>132.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:00:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>162644</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>5</td>\n",
       "      <td>749.12</td>\n",
       "      <td>103.79</td>\n",
       "      <td>240.38</td>\n",
       "      <td>149.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:01:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>162742</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>5</td>\n",
       "      <td>845.84</td>\n",
       "      <td>136.31</td>\n",
       "      <td>225.73</td>\n",
       "      <td>169.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:02:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>162808</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>9</td>\n",
       "      <td>1305.52</td>\n",
       "      <td>90.12</td>\n",
       "      <td>196.61</td>\n",
       "      <td>145.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:03:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>162943</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>3</td>\n",
       "      <td>568.89</td>\n",
       "      <td>138.45</td>\n",
       "      <td>232.02</td>\n",
       "      <td>189.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:04:07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           id                     api  count  res_time_sum  res_time_min  \\\n",
       "0  2019162542  /front-api/bill/create      8       1057.31         88.75   \n",
       "1      162644  /front-api/bill/create      5        749.12        103.79   \n",
       "2      162742  /front-api/bill/create      5        845.84        136.31   \n",
       "3      162808  /front-api/bill/create      9       1305.52         90.12   \n",
       "4      162943  /front-api/bill/create      3        568.89        138.45   \n",
       "\n",
       "   res_time_max  res_time_avg  interval         created_time  \n",
       "0        177.72         132.0        60  2018-11-01 00:00:07  \n",
       "1        240.38         149.0        60  2018-11-01 00:01:07  \n",
       "2        225.73         169.0        60  2018-11-01 00:02:07  \n",
       "3        196.61         145.0        60  2018-11-01 00:03:07  \n",
       "4        232.02         189.0        60  2018-11-01 00:04:07  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>api</th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>interval</th>\n",
       "      <th>created_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>153700</th>\n",
       "      <td>11455397</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>8</td>\n",
       "      <td>1203.44</td>\n",
       "      <td>70.51</td>\n",
       "      <td>232.43</td>\n",
       "      <td>150.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 19:27:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13721</th>\n",
       "      <td>1385806</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>7</td>\n",
       "      <td>1398.63</td>\n",
       "      <td>129.96</td>\n",
       "      <td>302.87</td>\n",
       "      <td>199.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-16 22:33:37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26647</th>\n",
       "      <td>2544879</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>14</td>\n",
       "      <td>2154.95</td>\n",
       "      <td>69.95</td>\n",
       "      <td>239.92</td>\n",
       "      <td>153.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-12-01 23:17:09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23556</th>\n",
       "      <td>2261987</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>7</td>\n",
       "      <td>1066.00</td>\n",
       "      <td>124.45</td>\n",
       "      <td>166.82</td>\n",
       "      <td>152.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-28 14:43:04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125202</th>\n",
       "      <td>9273166</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>1</td>\n",
       "      <td>153.41</td>\n",
       "      <td>153.41</td>\n",
       "      <td>153.41</td>\n",
       "      <td>153.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-03-30 10:09:14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              id                     api  count  res_time_sum  res_time_min  \\\n",
       "153700  11455397  /front-api/bill/create      8       1203.44         70.51   \n",
       "13721    1385806  /front-api/bill/create      7       1398.63        129.96   \n",
       "26647    2544879  /front-api/bill/create     14       2154.95         69.95   \n",
       "23556    2261987  /front-api/bill/create      7       1066.00        124.45   \n",
       "125202   9273166  /front-api/bill/create      1        153.41        153.41   \n",
       "\n",
       "        res_time_max  res_time_avg  interval         created_time  \n",
       "153700        232.43         150.0        60  2019-05-01 19:27:49  \n",
       "13721         302.87         199.0        60  2018-11-16 22:33:37  \n",
       "26647         239.92         153.0        60  2018-12-01 23:17:09  \n",
       "23556         166.82         152.0        60  2018-11-28 14:43:04  \n",
       "125202        153.41         153.0        60  2019-03-30 10:09:14  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#随机采样 可多次执行 查看不同数据\n",
    "df.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(179496, 9)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#查看数据条数\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                int64\n",
       "api              object\n",
       "count             int64\n",
       "res_time_sum    float64\n",
       "res_time_min    float64\n",
       "res_time_max    float64\n",
       "res_time_avg    float64\n",
       "interval          int64\n",
       "created_time     object\n",
       "dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#查看每列信息\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 179496 entries, 0 to 179495\n",
      "Data columns (total 9 columns):\n",
      "id              179496 non-null int64\n",
      "api             179496 non-null object\n",
      "count           179496 non-null int64\n",
      "res_time_sum    179496 non-null float64\n",
      "res_time_min    179496 non-null float64\n",
      "res_time_max    179496 non-null float64\n",
      "res_time_avg    179496 non-null float64\n",
      "interval        179496 non-null int64\n",
      "created_time    179496 non-null object\n",
      "dtypes: float64(4), int64(3), object(2)\n",
      "memory usage: 12.3+ MB\n"
     ]
    }
   ],
   "source": [
    "#查看占用内存大小\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count                     179496\n",
       "unique                         1\n",
       "top       /front-api/bill/create\n",
       "freq                      179496\n",
       "Name: api, dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#查看api中 判断 是否有不同值\n",
    "df['api'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([60], dtype=int64)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#判断 interval列的数据的种类 显示列表中只有一个元素 意思为只有一种类型\n",
    "df.interval.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#删除api列 节省占用空间 优化内存\n",
    "df = df.drop(['api', 'interval', 'id'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count                  179496\n",
       "unique                 179496\n",
       "top       2018-12-11 00:03:24\n",
       "freq                        1\n",
       "Name: created_time, dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#查看时间 判断是否有重复的\n",
    "df['created_time'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>created_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>153089</th>\n",
       "      <td>6</td>\n",
       "      <td>2105.08</td>\n",
       "      <td>125.74</td>\n",
       "      <td>992.46</td>\n",
       "      <td>350.0</td>\n",
       "      <td>2019-05-01 00:00:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153090</th>\n",
       "      <td>7</td>\n",
       "      <td>2579.11</td>\n",
       "      <td>76.55</td>\n",
       "      <td>987.47</td>\n",
       "      <td>368.0</td>\n",
       "      <td>2019-05-01 00:01:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153091</th>\n",
       "      <td>7</td>\n",
       "      <td>1277.79</td>\n",
       "      <td>109.65</td>\n",
       "      <td>236.73</td>\n",
       "      <td>182.0</td>\n",
       "      <td>2019-05-01 00:02:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153092</th>\n",
       "      <td>7</td>\n",
       "      <td>2137.20</td>\n",
       "      <td>131.55</td>\n",
       "      <td>920.52</td>\n",
       "      <td>305.0</td>\n",
       "      <td>2019-05-01 00:03:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153093</th>\n",
       "      <td>13</td>\n",
       "      <td>2948.70</td>\n",
       "      <td>86.42</td>\n",
       "      <td>491.31</td>\n",
       "      <td>226.0</td>\n",
       "      <td>2019-05-01 00:04:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153968</th>\n",
       "      <td>6</td>\n",
       "      <td>1083.97</td>\n",
       "      <td>70.85</td>\n",
       "      <td>262.22</td>\n",
       "      <td>180.0</td>\n",
       "      <td>2019-05-01 23:55:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153969</th>\n",
       "      <td>4</td>\n",
       "      <td>840.00</td>\n",
       "      <td>117.31</td>\n",
       "      <td>382.63</td>\n",
       "      <td>210.0</td>\n",
       "      <td>2019-05-01 23:56:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153970</th>\n",
       "      <td>2</td>\n",
       "      <td>295.51</td>\n",
       "      <td>101.71</td>\n",
       "      <td>193.80</td>\n",
       "      <td>147.0</td>\n",
       "      <td>2019-05-01 23:57:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153971</th>\n",
       "      <td>2</td>\n",
       "      <td>431.99</td>\n",
       "      <td>84.43</td>\n",
       "      <td>347.56</td>\n",
       "      <td>215.0</td>\n",
       "      <td>2019-05-01 23:58:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153972</th>\n",
       "      <td>3</td>\n",
       "      <td>428.84</td>\n",
       "      <td>103.58</td>\n",
       "      <td>206.57</td>\n",
       "      <td>142.0</td>\n",
       "      <td>2019-05-01 23:59:49</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>884 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        count  res_time_sum  res_time_min  res_time_max  res_time_avg  \\\n",
       "153089      6       2105.08        125.74        992.46         350.0   \n",
       "153090      7       2579.11         76.55        987.47         368.0   \n",
       "153091      7       1277.79        109.65        236.73         182.0   \n",
       "153092      7       2137.20        131.55        920.52         305.0   \n",
       "153093     13       2948.70         86.42        491.31         226.0   \n",
       "...       ...           ...           ...           ...           ...   \n",
       "153968      6       1083.97         70.85        262.22         180.0   \n",
       "153969      4        840.00        117.31        382.63         210.0   \n",
       "153970      2        295.51        101.71        193.80         147.0   \n",
       "153971      2        431.99         84.43        347.56         215.0   \n",
       "153972      3        428.84        103.58        206.57         142.0   \n",
       "\n",
       "               created_time  \n",
       "153089  2019-05-01 00:00:48  \n",
       "153090  2019-05-01 00:01:48  \n",
       "153091  2019-05-01 00:02:48  \n",
       "153092  2019-05-01 00:03:48  \n",
       "153093  2019-05-01 00:04:48  \n",
       "...                     ...  \n",
       "153968  2019-05-01 23:55:49  \n",
       "153969  2019-05-01 23:56:49  \n",
       "153970  2019-05-01 23:57:49  \n",
       "153971  2019-05-01 23:58:49  \n",
       "153972  2019-05-01 23:59:49  \n",
       "\n",
       "[884 rows x 6 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#截取一天的数据\n",
    "df[(df.created_time >= '2019-05-01') & (df.created_time < '2019-05-02')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#将索引修改为'created_time' 删除'created_time'列  修改索引类型为时间类型\n",
    "df.index = pd.to_datetime(df.created_time)\n",
    "df = df.drop('created_time', axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>created_time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-05-01 00:00:48</th>\n",
       "      <td>6</td>\n",
       "      <td>2105.08</td>\n",
       "      <td>125.74</td>\n",
       "      <td>992.46</td>\n",
       "      <td>350.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 00:01:48</th>\n",
       "      <td>7</td>\n",
       "      <td>2579.11</td>\n",
       "      <td>76.55</td>\n",
       "      <td>987.47</td>\n",
       "      <td>368.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 00:02:48</th>\n",
       "      <td>7</td>\n",
       "      <td>1277.79</td>\n",
       "      <td>109.65</td>\n",
       "      <td>236.73</td>\n",
       "      <td>182.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 00:03:48</th>\n",
       "      <td>7</td>\n",
       "      <td>2137.20</td>\n",
       "      <td>131.55</td>\n",
       "      <td>920.52</td>\n",
       "      <td>305.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 00:04:48</th>\n",
       "      <td>13</td>\n",
       "      <td>2948.70</td>\n",
       "      <td>86.42</td>\n",
       "      <td>491.31</td>\n",
       "      <td>226.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 23:55:49</th>\n",
       "      <td>6</td>\n",
       "      <td>1083.97</td>\n",
       "      <td>70.85</td>\n",
       "      <td>262.22</td>\n",
       "      <td>180.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 23:56:49</th>\n",
       "      <td>4</td>\n",
       "      <td>840.00</td>\n",
       "      <td>117.31</td>\n",
       "      <td>382.63</td>\n",
       "      <td>210.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 23:57:49</th>\n",
       "      <td>2</td>\n",
       "      <td>295.51</td>\n",
       "      <td>101.71</td>\n",
       "      <td>193.80</td>\n",
       "      <td>147.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 23:58:49</th>\n",
       "      <td>2</td>\n",
       "      <td>431.99</td>\n",
       "      <td>84.43</td>\n",
       "      <td>347.56</td>\n",
       "      <td>215.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 23:59:49</th>\n",
       "      <td>3</td>\n",
       "      <td>428.84</td>\n",
       "      <td>103.58</td>\n",
       "      <td>206.57</td>\n",
       "      <td>142.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>884 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     count  res_time_sum  res_time_min  res_time_max  \\\n",
       "created_time                                                           \n",
       "2019-05-01 00:00:48      6       2105.08        125.74        992.46   \n",
       "2019-05-01 00:01:48      7       2579.11         76.55        987.47   \n",
       "2019-05-01 00:02:48      7       1277.79        109.65        236.73   \n",
       "2019-05-01 00:03:48      7       2137.20        131.55        920.52   \n",
       "2019-05-01 00:04:48     13       2948.70         86.42        491.31   \n",
       "...                    ...           ...           ...           ...   \n",
       "2019-05-01 23:55:49      6       1083.97         70.85        262.22   \n",
       "2019-05-01 23:56:49      4        840.00        117.31        382.63   \n",
       "2019-05-01 23:57:49      2        295.51        101.71        193.80   \n",
       "2019-05-01 23:58:49      2        431.99         84.43        347.56   \n",
       "2019-05-01 23:59:49      3        428.84        103.58        206.57   \n",
       "\n",
       "                     res_time_avg  \n",
       "created_time                       \n",
       "2019-05-01 00:00:48         350.0  \n",
       "2019-05-01 00:01:48         368.0  \n",
       "2019-05-01 00:02:48         182.0  \n",
       "2019-05-01 00:03:48         305.0  \n",
       "2019-05-01 00:04:48         226.0  \n",
       "...                           ...  \n",
       "2019-05-01 23:55:49         180.0  \n",
       "2019-05-01 23:56:49         210.0  \n",
       "2019-05-01 23:57:49         147.0  \n",
       "2019-05-01 23:58:49         215.0  \n",
       "2019-05-01 23:59:49         142.0  \n",
       "\n",
       "[884 rows x 5 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#使用时间类型字符串查找指定数据\n",
    "df['2019-5-1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>179496.000000</td>\n",
       "      <td>179496.000000</td>\n",
       "      <td>179496.000000</td>\n",
       "      <td>179496.000000</td>\n",
       "      <td>179496.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7.175909</td>\n",
       "      <td>1393.177832</td>\n",
       "      <td>108.419626</td>\n",
       "      <td>359.880374</td>\n",
       "      <td>187.812208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.325160</td>\n",
       "      <td>1499.486073</td>\n",
       "      <td>79.640693</td>\n",
       "      <td>638.919827</td>\n",
       "      <td>224.464813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>36.550000</td>\n",
       "      <td>3.210000</td>\n",
       "      <td>36.550000</td>\n",
       "      <td>36.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>607.707500</td>\n",
       "      <td>83.410000</td>\n",
       "      <td>198.280000</td>\n",
       "      <td>144.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.000000</td>\n",
       "      <td>1154.905000</td>\n",
       "      <td>97.120000</td>\n",
       "      <td>256.090000</td>\n",
       "      <td>167.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>1834.117500</td>\n",
       "      <td>116.990000</td>\n",
       "      <td>374.410000</td>\n",
       "      <td>202.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>31.000000</td>\n",
       "      <td>142650.550000</td>\n",
       "      <td>18896.640000</td>\n",
       "      <td>142468.270000</td>\n",
       "      <td>71325.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               count   res_time_sum   res_time_min   res_time_max  \\\n",
       "count  179496.000000  179496.000000  179496.000000  179496.000000   \n",
       "mean        7.175909    1393.177832     108.419626     359.880374   \n",
       "std         4.325160    1499.486073      79.640693     638.919827   \n",
       "min         1.000000      36.550000       3.210000      36.550000   \n",
       "25%         4.000000     607.707500      83.410000     198.280000   \n",
       "50%         7.000000    1154.905000      97.120000     256.090000   \n",
       "75%        10.000000    1834.117500     116.990000     374.410000   \n",
       "max        31.000000  142650.550000   18896.640000  142468.270000   \n",
       "\n",
       "        res_time_avg  \n",
       "count  179496.000000  \n",
       "mean      187.812208  \n",
       "std       224.464813  \n",
       "min        36.000000  \n",
       "25%       144.000000  \n",
       "50%       167.000000  \n",
       "75%       202.000000  \n",
       "max     71325.000000  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#查看数据 分析数据\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#绘制count的直方图 展示信息 \n",
    "#结论 大部分请求每分钟调用10次以内\n",
    "df['count'].hist( bins= 30)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#绘制一天中接口请求信息\n",
    "#结论 凌晨时间访问较少 下午与晚上为访问高峰\n",
    "df['2019-5-1']['count'].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#重新采样数据 汇总为1小时的平均数据\n",
    "df1 = df['2019-5-1']\n",
    "#获取每小时请求的平均值 绘制plot\n",
    "df1['count'].resample('1H').mean().plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#设置图片大小\n",
    "plt.figure(figsize = (15,4))\n",
    "df1['count'].resample('1H').mean().plot(kind='bar')\n",
    "#设置文字旋转角度为60度\n",
    "#plt.xticks(rotation = 60)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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LwX+2iS5mgL+NiDMppXLn/wKpOV5GKEmZcgpFkjJlgEtSpgxwScqUAS5JmTLAJSlTBrgkZcoAl6RMGeCSlKn/A6+SRiLXU0nxAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#使用箱线图 分析有没有时段 数显异常的多次请求接口情况\n",
    "df['2019-5-1'][['count']].boxplot(showmeans = True, meanline = True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>created_time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-11-01 20:47:09</th>\n",
       "      <td>21</td>\n",
       "      <td>3117.20</td>\n",
       "      <td>84.90</td>\n",
       "      <td>260.82</td>\n",
       "      <td>148.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 21:03:09</th>\n",
       "      <td>21</td>\n",
       "      <td>3706.20</td>\n",
       "      <td>78.12</td>\n",
       "      <td>321.47</td>\n",
       "      <td>176.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 21:13:09</th>\n",
       "      <td>24</td>\n",
       "      <td>4602.03</td>\n",
       "      <td>76.31</td>\n",
       "      <td>391.12</td>\n",
       "      <td>191.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-02 21:34:11</th>\n",
       "      <td>30</td>\n",
       "      <td>4610.15</td>\n",
       "      <td>72.49</td>\n",
       "      <td>463.41</td>\n",
       "      <td>153.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-03 14:20:13</th>\n",
       "      <td>21</td>\n",
       "      <td>3113.93</td>\n",
       "      <td>74.29</td>\n",
       "      <td>266.20</td>\n",
       "      <td>148.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-30 21:33:21</th>\n",
       "      <td>27</td>\n",
       "      <td>6456.64</td>\n",
       "      <td>99.65</td>\n",
       "      <td>978.91</td>\n",
       "      <td>239.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-30 21:43:21</th>\n",
       "      <td>21</td>\n",
       "      <td>6371.84</td>\n",
       "      <td>65.98</td>\n",
       "      <td>1175.37</td>\n",
       "      <td>303.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-30 21:47:21</th>\n",
       "      <td>21</td>\n",
       "      <td>3992.83</td>\n",
       "      <td>87.83</td>\n",
       "      <td>440.88</td>\n",
       "      <td>190.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-30 21:53:21</th>\n",
       "      <td>24</td>\n",
       "      <td>8467.02</td>\n",
       "      <td>120.22</td>\n",
       "      <td>1511.17</td>\n",
       "      <td>352.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-30 22:17:21</th>\n",
       "      <td>21</td>\n",
       "      <td>4926.35</td>\n",
       "      <td>85.01</td>\n",
       "      <td>826.90</td>\n",
       "      <td>234.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>746 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     count  res_time_sum  res_time_min  res_time_max  \\\n",
       "created_time                                                           \n",
       "2018-11-01 20:47:09     21       3117.20         84.90        260.82   \n",
       "2018-11-01 21:03:09     21       3706.20         78.12        321.47   \n",
       "2018-11-01 21:13:09     24       4602.03         76.31        391.12   \n",
       "2018-11-02 21:34:11     30       4610.15         72.49        463.41   \n",
       "2018-11-03 14:20:13     21       3113.93         74.29        266.20   \n",
       "...                    ...           ...           ...           ...   \n",
       "2019-05-30 21:33:21     27       6456.64         99.65        978.91   \n",
       "2019-05-30 21:43:21     21       6371.84         65.98       1175.37   \n",
       "2019-05-30 21:47:21     21       3992.83         87.83        440.88   \n",
       "2019-05-30 21:53:21     24       8467.02        120.22       1511.17   \n",
       "2019-05-30 22:17:21     21       4926.35         85.01        826.90   \n",
       "\n",
       "                     res_time_avg  \n",
       "created_time                       \n",
       "2018-11-01 20:47:09         148.0  \n",
       "2018-11-01 21:03:09         176.0  \n",
       "2018-11-01 21:13:09         191.0  \n",
       "2018-11-02 21:34:11         153.0  \n",
       "2018-11-03 14:20:13         148.0  \n",
       "...                           ...  \n",
       "2019-05-30 21:33:21         239.0  \n",
       "2019-05-30 21:43:21         303.0  \n",
       "2019-05-30 21:47:21         190.0  \n",
       "2019-05-30 21:53:21         352.0  \n",
       "2019-05-30 22:17:21         234.0  \n",
       "\n",
       "[746 rows x 5 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#查看请求超过20的时段\n",
    "df[df['count'] > 20 ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#查看5月1的平均相应时间\n",
    "df['2019-5-1']['res_time_avg'].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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nncWYMWM466yzmD17drVLMhswd0SapXD66adzxx13cPvttzN79my2bNnCV7/6VU4//fRql2Y2IA59sxQmTJjA+++/z/Llyzlw4ACnnHIK48aNY8KECdUuzWxA3L1jlsL27duZNGkSjY2NSKKxsZFJkyaxffv2apdmNiAOfbMUxo0bx/Lly9m2bRvr169n27ZtLF++nHHjxlW7NLMB8egdsxTGjBnDlClTmDRp0pGLs/bu3cvbb7/N4cOHq12e2REevWM2BBobGzlw4ABQHL4JcODAARoby04jZTbq+ESuWUoTJkzgnnvuOTK18hVXXFHtkswGzKFvlsKOHTtYtmwZixcvPnJx1jXXXMNdd91V7dLMBsTdO2YpTJs2jVwux9SpUxkzZgxTp04ll8sxbdq0apdmNiA+0jdL4b333uOdd96hvr6eiOD999/nnXfeYcwYHz9ZbfA31SyFXbt2MXnyZMaPHw/A+PHjmTx5Mrt27apyZWYD49A3S+niiy9m4sSJSGLixIlcfPHF1S7JbMDcvWOW0gMPPMAdd9xxZO6dW2+9tdolmQ2YQ98shbq6OsaOHXvU3DunnHIKhw751hBWG9y9Y5bCwYMHOXDgAFOmTDlyde6BAwc4ePBgtUszGxCHvlkK9fX1tLa2MmXKFACmTJlCa2ur75xlNcOhb5bC/v37Wbt2Lfv27QNg3759rF27lv3791e5MrOBcZ++WQqNjY3s3LmTt956C4DXXnuNcePGee4dqxmV3hj9teQm6M9L6k7aTpf0pKRfJ8+/VbL8bZK2SnpZ0oWVFm820nbv3s3+/fuPXIw1ZswY9u/fz+7du6tcmdnADEX3TktEzCuZwnM5sC4iZgHrkvdImg0sAeYAFwErJI0dgv2bjZi+bp2+aZT7nvvazUa74ejTvxS4N3l9L3BZSfv9EdEbEduArcB5w7B/s2FXeqRvVksq7dMP4KeSArgrIlYCDRHxBkBEvCHp48myjcAzJev2JG3HkbQUWArQ0NBAPp+vsEyzoXXskT7g76nVhEpD/1MRsSMJ9iclvXSSZVWmrextu5JfHiuheOeshQsXVlim2dA69dRT+eCDD448A/h7arWgor9NI2JH8rwTeIRid82bkqYCJM87k8V7gDNLVp8O7Khk/2bV0hf0fc9mtWLQoS9poqTT+l4Dnwc2AauBq5LFrgIeTV6vBpZIqpc0E5gFPDvY/ZuZWXqVdO80AI8k9wmtA/5XRDwh6RfAg5LagH8EvggQEZslPQhsAQ4CN0aEJywxMxtBgw79iHgVOLdM+9vAohOs0wF0DHafZmZWGY83MzPLEIe+mVmGOPTNzDLEoW9mliEOfTOzDHHomw2C596xWuX59M0SyTUnA1Ju7p2Brh9RdvYRsxHh0DdLDCSMTxbsDnOrBf7b1CyFm266KVW72WjjI32zFL73ve8BcPfdd9Pb20t9fT3XXXfdkXaz0U6j/U/S5ubm6O7urnYZZseZsfxxXvvmH1S7DLOyJG0suaPhEe7eMTPLEIe+mVmGOPTNzDLEoW9mliEOfTOzDHHom5lliEPfzCxDHPpmZhky6NCXdKakDZIKkjZL+nLS/nVJ2yU9nzwuLlnnNklbJb0s6cKh+AHMzGzgKpmG4SBwS0Q8J+k0YKOkJ5PPvhMR3ypdWNJsYAkwB5gG/EzS2RFxqIIazMwshUEf6UfEGxHxXPL6XaAANJ5klUuB+yOiNyK2AVuB8wa7fzMzS29IJlyTNAP4XeD/AJ8CbpJ0JdBN8a+B3RR/ITxTsloPJ/glIWkpsBSgoaGBfD4/FGVahty4bh/7Dgz/fmYsf3xYtz/xFPjrRROHdR+WLRWHvqRJwMPAVyJij6Q7gW8AkTx/G7gGKDcRednZ3iJiJbASihOuLVy4sNIyLWP2PTH8k6Hl83mG+7s5Y/njw74Py5aKRu9IOoVi4P8wIn4EEBFvRsShiDgM3M3/78LpAc4sWX06sKOS/ZuZWTqVjN4R0AkUIuJ/lrRPLVnscmBT8no1sERSvaSZwCzg2cHu38zM0quke+dTwJ8CL0p6Pmn7T0CrpHkUu25eA5YBRMRmSQ8CWyiO/LnRI3fMzEbWoEM/Iroo30//k5Os0wF0DHafZmZWGV+Ra2aWIb5Hrn0onda0nH957/Lh39G9w7v505oAfEtGGzoOfftQerfwzQ/NkE2zoeTuHTOzDHHom5lliLt37ENrRLpGnhjefXxk/CnDun3LHoe+fSgNd38+FH+pjMR+zIaSu3fMzDLEoW9mliEOfTOzDHHom5lliEPfzCxDHPpmZhni0DczyxCHvplZhvjiLLNE8WZwKdf5H+n3E1H21tBmI8JH+maJiEj12LBhQ+p1HPhWbQ59M7MMceibmWXIiIe+pIskvSxpq6QRuLWRmZn1GdHQlzQW+GtgMTAbaJU0eyRrMDPLspE+0j8P2BoRr0bEfuB+4NIRrsHMLLNGOvQbgd+UvO9J2szMbASM9Dj9cgOhjxvDJmkpsBSgoaGBfD4/zGWZpbd3715/N63mjHTo9wBnlryfDuw4dqGIWAmsBGhubo6FCxeOSHFmaeTzefzdtFqjkbxYRFId8A/AImA78Avg30bE5pOs80/A6yNToVkqZwBvVbsIsxP4RER87NjGET3Sj4iDkm4C1gJjgXtOFvjJOscVbTYaSOqOiOZq12GWxoge6Zt9mDj0rRb5ilwzswxx6JsN3spqF2CWlrt3zMwyxEf6ZmYZ4tA3M8sQh76ZWYY49M0Ski4rnfVV0l9K+lw1azIbaj6Rax8KKt7gVhFxuIJtrAJ+HBEPDVlhZqOMj/StZkmaIakgaQXwHPCnkp6W9Jykv5E0KVnum5K2SHpB0rdOsK1PApcAd0h6XtI/l7RK0h8ln78m6b8l2++W9HuS1kp6RdL1Jdu5VdIvkn39l37q/1tJGyVtTiYZRNINkm4vWeZqSd9LXv9nSS9JelJSTtJ/rOxf0LLIoW+17hzgPuACoA34XET8HtAN/AdJpwOXA3Mi4neA/1puIxHxc2A1cGtEzIuIV8os9puI+H3g74BVwB8B5wN/CSDp88AsiveNmAfMl/SZk9R+TUTMB5qBL0maAjwE/GHJMn8CPCCpGfg3wO8mn/tKYBuUkZ5l02yovR4Rz0j6AsW7sf19saeHccDTwB7gA+AHkh4HflzBvlYnzy8CkyLiXeBdSR9I+ijw+eTxy2S5SRR/CTx1gu19SdLlyeszgVnJz/KqpPOBX1P8pfb3wJeBRyPifQBJj1Xwc1iGOfSt1u1LngU8GRGtxy4g6TyKM7suAW4CPjvIffUmz4dLXve9r0tq+O8RcVd/G5K0EPgc8PsR8Z6kPHBq8vEDwB8DLwGPREQk5yzMKubuHfuweAb4lKSzACRNkHR20q//kYj4CfAVit0uJ/IucFoFNawFrik5l9Ao6eMnWPYjwO4k8P8FxW6iPj8CLgNaKf4CAOgC/rWkU5Pt/0EFdVqG+UjfPhQi4p8kXQ3kJNUnzX9OMcgflXQqxSPxPzvJZu4H7pb0JYr99Wlr+KmkJuDp5MB8L/DvgJ1lFn8CuF7SC8DLFH9p9W1nt6QtwOyIeDZp+4Wk1cCvKN5foht4J22NZh6yaVYjJE2KiL2SJlA8T7A0Ip6rdl1WW3ykb1Y7ViYXj50K3OvAt8Hwkb5ljqR24IvHNP9NRHQMw76mAOvKfLQoIt4e6v2Z9cehb2aWIR69Y2aWIQ59M7MMceibmWWIQ9/MLEP+HxKWBHnfIgDqAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#使用箱线图 查看异常值\n",
    "df['2019-5-1'][['res_time_avg']].boxplot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>created_time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-05-01 00:34:48</th>\n",
       "      <td>1</td>\n",
       "      <td>1694.47</td>\n",
       "      <td>1694.47</td>\n",
       "      <td>1694.47</td>\n",
       "      <td>1694.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 14:00:49</th>\n",
       "      <td>17</td>\n",
       "      <td>19770.18</td>\n",
       "      <td>207.54</td>\n",
       "      <td>2974.52</td>\n",
       "      <td>1162.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 18:36:49</th>\n",
       "      <td>8</td>\n",
       "      <td>8799.92</td>\n",
       "      <td>96.59</td>\n",
       "      <td>3233.26</td>\n",
       "      <td>1099.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 19:09:49</th>\n",
       "      <td>6</td>\n",
       "      <td>7399.94</td>\n",
       "      <td>307.39</td>\n",
       "      <td>3153.02</td>\n",
       "      <td>1233.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 19:10:49</th>\n",
       "      <td>13</td>\n",
       "      <td>23595.60</td>\n",
       "      <td>206.20</td>\n",
       "      <td>4664.84</td>\n",
       "      <td>1815.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 20:38:49</th>\n",
       "      <td>15</td>\n",
       "      <td>16169.25</td>\n",
       "      <td>142.47</td>\n",
       "      <td>3624.26</td>\n",
       "      <td>1077.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     count  res_time_sum  res_time_min  res_time_max  \\\n",
       "created_time                                                           \n",
       "2019-05-01 00:34:48      1       1694.47       1694.47       1694.47   \n",
       "2019-05-01 14:00:49     17      19770.18        207.54       2974.52   \n",
       "2019-05-01 18:36:49      8       8799.92         96.59       3233.26   \n",
       "2019-05-01 19:09:49      6       7399.94        307.39       3153.02   \n",
       "2019-05-01 19:10:49     13      23595.60        206.20       4664.84   \n",
       "2019-05-01 20:38:49     15      16169.25        142.47       3624.26   \n",
       "\n",
       "                     res_time_avg  \n",
       "created_time                       \n",
       "2019-05-01 00:34:48        1694.0  \n",
       "2019-05-01 14:00:49        1162.0  \n",
       "2019-05-01 18:36:49        1099.0  \n",
       "2019-05-01 19:09:49        1233.0  \n",
       "2019-05-01 19:10:49        1815.0  \n",
       "2019-05-01 20:38:49        1077.0  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#查看大于1000的平均时间响应\n",
    "#分析异常的平局时间\n",
    "#2019-05-01 00:34:48 为异常值 \n",
    "df1 = df['2019-5-1']\n",
    "df1[df1['res_time_avg'] > 1000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#分析20分钟的平均值的响应时长\n",
    "#业务高峰时段相应时间上升\n",
    "df1.resample('20T').mean()[['res_time_sum',\t'res_time_min',\t'res_time_max',\t'res_time_avg']].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#查看10天的接口调用情况\n",
    "#结论 每天高峰时间类似\n",
    "plt.figure(figsize = (15,4))\n",
    "df['2019-5-1':'2019-5-10']['count'].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "#对数据添加列 为星期属性 0为周1 1为周2\n",
    "#调用时间索引的weekday方法\n",
    "df['weekday'] = df.index.weekday"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "#对数据添加列 是否是周末\n",
    "#通过判断weekday列的值是否是5或6 返回bool值 赋值给weekend列\n",
    "df['weekend'] = df['weekday'].isin({5,6})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "weekend\n",
       "False    7.016846\n",
       "True     7.574989\n",
       "Name: count, dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#对否是周末进行分钟 判断周末是否影响接口的调用\n",
    "#周末的平均值高于非周末\n",
    "df.groupby('weekend')['count'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>weekend</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>created_time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.239120</td>\n",
       "      <td>3.467782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.668388</td>\n",
       "      <td>1.741849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.162551</td>\n",
       "      <td>1.161826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.086705</td>\n",
       "      <td>1.050000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.155556</td>\n",
       "      <td>1.076923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.136364</td>\n",
       "      <td>1.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.071429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.080000</td>\n",
       "      <td>1.144928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.239011</td>\n",
       "      <td>1.254111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2.031690</td>\n",
       "      <td>1.992958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4.195845</td>\n",
       "      <td>4.031889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>6.668042</td>\n",
       "      <td>6.905772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>8.260503</td>\n",
       "      <td>8.851321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>8.934448</td>\n",
       "      <td>9.858422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>8.466504</td>\n",
       "      <td>9.420550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>6.784996</td>\n",
       "      <td>7.334743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>6.717731</td>\n",
       "      <td>7.342150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>8.655913</td>\n",
       "      <td>9.270430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>10.536496</td>\n",
       "      <td>11.173609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>10.846906</td>\n",
       "      <td>11.695043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>9.034164</td>\n",
       "      <td>10.419916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>5.946834</td>\n",
       "      <td>7.025452</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "weekend           False      True \n",
       "created_time                      \n",
       "0              3.239120   3.467782\n",
       "1              1.668388   1.741849\n",
       "2              1.162551   1.161826\n",
       "3              1.086705   1.050000\n",
       "4              1.155556   1.076923\n",
       "5              1.136364   1.333333\n",
       "6              1.000000   1.000000\n",
       "7              1.000000   1.000000\n",
       "8              1.000000   1.071429\n",
       "9              1.080000   1.144928\n",
       "10             1.239011   1.254111\n",
       "11             2.031690   1.992958\n",
       "12             4.195845   4.031889\n",
       "13             6.668042   6.905772\n",
       "14             8.260503   8.851321\n",
       "15             8.934448   9.858422\n",
       "16             8.466504   9.420550\n",
       "17             6.784996   7.334743\n",
       "18             6.717731   7.342150\n",
       "19             8.655913   9.270430\n",
       "20            10.536496  11.173609\n",
       "21            10.846906  11.695043\n",
       "22             9.034164  10.419916\n",
       "23             5.946834   7.025452"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#获得周末与非周末24个小时每小时平均请求次数\n",
    "#df.groupby(['weekend', df.index.hour])['count'].mean().unstack().T\n",
    "df.groupby(['weekend', df.index.hour])['count'].mean().unstack(level = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby(['weekend', df.index.hour])['count'].mean().unstack(level = 0).plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
